Oral Fc-Coupled Preproinsulin Achieves Systemic and Thymic Delivery Through the Neonatal Fc Receptor and Partially Delays Autoimmune Diabetes
Bibliographic record
Abstract
Tolerogenic vaccinations using beta-cell antigens are attractive for type 1 diabetes prevention, but clinical trials have been disappointing. This is probably due to the late timing of intervention, when multiple auto-antibodies are already present. We therefore devised a strategy to introduce the initiating antigen preproinsulin (PPI) during neonatal life, when autoimmunity is still silent and central tolerance mechanisms, which remain therapeutically unexploited, are more active. This strategy employs an oral administration of PPI-Fc, i.e. PPI fused with an IgG Fc to bind the intestinal neonatal Fc receptor (FcRn) that physiologically delivers maternal antibodies to the offspring during breastfeeding. Neonatal oral PPI-Fc vaccination did not prevent diabetes development in PPI T-cell receptor-transgenic G9C8.NOD mice. However, PPI-Fc was efficiently transferred through the intestinal epithelium in an Fc- and FcRn-dependent manner, was taken up by antigen presenting cells, and reached the spleen and thymus. Although not statistically significant, neonatal oral PPI-Fc vaccination delayed diabetes onset in polyclonal Ins2-/-.NOD mice that spontaneously develop accelerated diabetes. Thus, this strategy shows promise in terms of systemic and thymic antigen delivery via the intestinal FcRn pathway, but the current PPI-Fc formulation/regimen requires further improvements to achieve diabetes prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".